1. Field of the Invention
Embodiments of the invention generally relate to the field of automated translation of natural-language sentences using linguistic descriptions and various applications in such areas as automated abstracting, machine translation, natural language processing, control systems, information search (including on the Internet), semantic Web, computer-aided learning, expert systems, speech recognition/synthesis and others.
2. Description of the Related Art
Prior machine translation (MT) systems differ in the approaches and methods that they use and also in their abilities to recognize various complex language constructs and produce quality translation of texts from one language into another. According to their core principles, these systems can be divided into the following groups.
One of the traditional approaches is based on translation rules or transformation rules and is called Rule-Based Mont. (RBMT). This approach, however, is rather limited when it comes to working with complex language phenomena. In the recent years no significant breakthroughs have been achieved within this field. The best known systems of this type are SYSTRAN and PROMPT. The known RBMT systems, however, usually possess restricted syntactic models and simplified dictionary descriptions where language ambiguities are artificially removed.
Rule-based MT has evolved into Model-Based Mont. (MBMT) which is based on linguistic models. Implementing a MBMT system to produce quality translation demands considerable effort to create linguistic models and corresponding descriptions for specific languages. Evolution of MBMT systems is connected with developing complex language models on all levels of language descriptions. The need in today's modern world requires translation between many different languages. Creating such MBMT systems is only possible within a large-scale project to integrate the results of engineering and linguistic research.
Another traditional approach is Knowledge-Based Mont. (KBMT) which uses semantic descriptions. While the MBMT approach is based on knowledge about a language, the KBMT approach considers translation as a process of understanding based on real knowledge about the World. Presently, interest in Knowledge-Based Machine Translation (KBMT) has been waning.